This paper presents a method for computing interleaved additive and subtractive manufacturing operations to fabricate models of arbitrary shapes. We solve the manufacturing planning problem by searching a sequence of inverse operations that progressively transform a target model into a null shape. Each inverse operation corresponds to either an additive or a subtractive step, ensuring both manufacturability and structural stability of intermediate shapes throughout the process. We theoretically prove that any model can be fabricated exactly using a sequence generated by our approach. To demonstrate the effectiveness of this method, we adopt a voxel-based implementation and develop a scalable algorithm that works on models represented by a large number of voxels. Our approach has been tested across a range of digital models and further validated through physical fabrication on a hybrid manufacturing system with automatic tool switching.

The estimation of the 6D pose of industrial components is essential for smart manufacturing. Especially for complex units that require intensive manual operations, such as a concentrator photovoltaics solar panel, accurate spatial localization provides visual aids for industrial automation. In this paper, we propose an accurate and scalable framework to address the dimensional variability of industrial components and tackle practical implementation issues. First, we use the scalable architecture EfficientNet as the backbone coupled with an enhanced feature pyramid network to estimate the object’s pose. By introducing vertical and horizontal connections of shallow layers, the feature extraction of small objects is optimized for better detection accuracy. Second, leveraging the reliable 2D detection results and geometry information, an adaptive pose refinement module is designed to adjust the estimated 6D pose. The scaling of the backbone network and the computational complexity of refined modules are uniformly adjusted via a shared hyperparameter, resulting in a globally scalable framework. In terms of the pose estimation accuracy, the effectiveness of the refinement module and the real-time performance, validations are conducted both on the LINEMOD dataset and our customized datasets comprising of objects from the industrial photovoltaic system. Additionally, to further illustrate the effectiveness of the proposed method, a precision parallel robot is employed to validate the accuracy of real-time object pose tracking.

Brain-computer interface (BCI) can provide a direct communication path between the human brain and an external device. The steady-state visual evoked potential (SSVEP)-based BCI has been widely explored in the past decades due to its high signal-to-noise ratio and fast communication rate. Several spatial filtering methods have been developed for frequency detection. However the temporal knowledge contained in the SSVEP signal is not effectively utilized. In this study, we propose a canonical correlation analysis (CCA)-based spatio-temporal filtering method to improve target classification. The training signal and two types of template signals (i.e. individual template and artificial sine-cosine reference) are first augmented via temporal information. Three sets of augmented data are then concatenated by trials. The CCA is performed twice, between the newly obtained training data and each template. The trained four spatial filters can be applied in the following test process. A public benchmark dataset was used to evaluate the performance of the proposed method and the other three comparing methods, such as CCA, MsetCCA, and TRCA. The experimental results indicate that the proposed method yields significantly higher performance. This paper also explored the effects of the number of electrodes and training blocks on classification accuracy. The results further demonstrated the effectiveness of the proposed method in SSVEP detection.

In this paper, we propose a novel iterative learning control (ILC) scheme for precise state tracking of pneumatic muscle (PM) actuators. Two critical issues are considered in our scheme: 1) state constraints on PM position and velocity; 2) uncertainties of the PM model. Based on the three-element form, a PM model is constructed that takes both parametric and nonparametric uncertainties into consideration. By introducing the composite energy function (CEF) approach incorporated with a barrier Lyapunov function (BLF), full state constraints of PM will not be violated and uncertainties are effectively compensated. Through rigorous analysis, we show that under proposed ILC scheme, uniform convergence of PM state tracking errors are guaranteed. Simulation results validate the performance of the proposed scheme.